Search Results for author: Jinho Lee

Found 17 papers, 5 papers with code

ETF Portfolio Construction via Neural Network trained on Financial Statement Data

no code implementations4 Jul 2022 Jinho Lee, Sungwoo Park, Jungyu Ahn, Jonghun Kwak

Therefore, we use the data of individual stocks to train our neural networks to predict the future performance of individual stocks and use these predictions and the portfolio deposit file (PDF) to construct a portfolio of ETFs.

Shai-am: A Machine Learning Platform for Investment Strategies

no code implementations1 Jul 2022 Jonghun Kwak, Jungyu Ahn, Jinho Lee, Sungwoo Park

The finance industry has adopted machine learning (ML) as a form of quantitative research to support better investment decisions, yet there are several challenges often overlooked in practice.

ConCoDE: Hard-constrained Differentiable Co-Exploration Method for Neural Architectures and Hardware Accelerators

no code implementations29 Sep 2021 Deokki Hong, Kanghyun Choi, Hey Yoon Lee, Joonsang Yu, Youngsok Kim, Noseong Park, Jinho Lee

To handle the hard constraint problem of differentiable co-exploration, we propose ConCoDE, which searches for hard-constrained solutions without compromising the global design objectives.

Neural Architecture Search

An Attention Module for Convolutional Neural Networks

no code implementations18 Aug 2021 Zhu Baozhou, Peter Hofstee, Jinho Lee, Zaid Al-Ars

To solve the two problems together, we initially propose an attention module for convolutional neural networks by developing an AW-convolution, where the shape of attention maps matches that of the weights rather than the activations.

Image Classification object-detection +1

GradPIM: A Practical Processing-in-DRAM Architecture for Gradient Descent

no code implementations15 Feb 2021 Heesu Kim, Hanmin Park, Taehyun Kim, Kwanheum Cho, Eojin Lee, Soojung Ryu, Hyuk-Jae Lee, Kiyoung Choi, Jinho Lee

In this paper, we present GradPIM, a processing-in-memory architecture which accelerates parameter updates of deep neural networks training.

DANCE: Differentiable Accelerator/Network Co-Exploration

no code implementations14 Sep 2020 Kanghyun Choi, Deokki Hong, Hojae Yoon, Joonsang Yu, Youngsok Kim, Jinho Lee

In such circumstances, this work presents DANCE, a differentiable approach towards the co-exploration of the hardware accelerator and network architecture design.

Neural Architecture Search

SoFAr: Shortcut-based Fractal Architectures for Binary Convolutional Neural Networks

no code implementations11 Sep 2020 Zhu Baozhou, Peter Hofstee, Jinho Lee, Zaid Al-Ars

Inspired by the shortcuts and fractal architectures, we propose two Shortcut-based Fractal Architectures (SoFAr) specifically designed for BCNNs: 1. residual connection-based fractal architectures for binary ResNet, and 2. dense connection-based fractal architectures for binary DenseNet.


MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System

no code implementations10 Jul 2020 Jinho Lee, Raehyun Kim, Seok-Won Yi, Jaewoo Kang

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest.

Multi-agent Reinforcement Learning reinforcement-learning

SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders

no code implementations14 Jan 2020 Inseok Hwang, Jinho Lee, Frank Liu, Minsik Cho

Our intuition is that, the more similarity exists between the unknown data samples and the part of known data that an autoencoder was trained with, the better chances there could be that this autoencoder makes use of its trained knowledge, reconstructing output samples closer to the originals.

Data Augmentation

MUTE: Data-Similarity Driven Multi-hot Target Encoding for Neural Network Design

no code implementations15 Oct 2019 Mayoore S. Jaiswal, Bumboo Kang, Jinho Lee, Minsik Cho

Target encoding is an effective technique to deliver better performance for conventional machine learning methods, and recently, for deep neural networks as well.

General Classification Image Classification

Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network

1 code implementation28 Feb 2019 Jinho Lee, Raehyun Kim, Yookyung Koh, Jaewoo Kang

Moreover, the results show that future stock prices can be predicted even if the training and testing procedures are done in different countries.

Stock Market Prediction

Globally Optimal Object Tracking with Fully Convolutional Networks

no code implementations25 Dec 2016 Jinho Lee, Brian Kenji Iwana, Shouta Ide, Seiichi Uchida

Thus, we propose a new and robust tracking method using a Fully Convolutional Network (FCN) to obtain an object probability map and Dynamic Programming (DP) to seek the globally optimal path through all frames of video.

Computer Vision Object Tracking

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